The present invention relates to method and device of an object detectable and background removal and storage media for storing therein a program thereof for the sake of obtaining of an object with a background removed from an image. More to particularly this invention relates to method and device of object detectable and background removal and storage media for storing program for the sake of obtaining of an object from the image having virtually even background.
A method and a device of object detectable and background removal, and a storage media for storing program thereof is applicable to various field of application. The object detectable and background removal method is based on a technology to isolate an object from an image with a virtually even background. The object is taken a photograph with the exception of a background area. FIG. 1 is a view showing one example of a processing target image. In the input image signal 1 of FIG. 1, a target object 3 whose photograph is taken is shown in virtually even background area 2. If it is capable of being isolated completely only the target object 3 while removing the background area 2, it is capable of being applied to the image composition technique in the field of computer vision instead of the object recognition processing, the robot visual sensation, or the chromakey technique.
In the television program, a scene in which a person is mated with another background is often televised. The chromakey technique which is one of techniques for cutting a target object out of the background is in use for the television station frequently. The chromakey is described in accordance with the literature: xe2x80x9cTelevision Image Rejection Engineering Handbookxe2x80x9d (Television Society, pp. 704, 1990).
One of the techniques for isolating the target object to be removed the background therefrom is described in accordance with the literature: Japanese Patent Application Laid-Open No. HEI 2-206885 xe2x80x9cImage Processing Devicexe2x80x9d. In the present literature, by way of the processing target, there is supposed that the image is composed of the background having intermediate brightness, and the object which comes to be target of cutting consists of either a brighter area than the background or a darker area than the background. The threshold which is established by some way or other is utilized, subsequently, implementing the threshold processing for isolating the pixel with brightness value beyond the threshold, thus isolating the pixel which is brighter than the background. Similarly, by virtue of the threshold processing of utilizing the threshold established separately, thus isolating the darker pixel than the back ground. Thus, it is capable of being isolated the whole object by synthesizing the results of the two threshold processing.
There is described one of the techniques for calculating stably the characteristic quantity such as the center of gravity location or the area thereof from the target object isolated out of the background. It is described in accordance with the literature: the Japanese Patent Publication No. HEI 4-116778 xe2x80x9cImage Processing Methodxe2x80x9d.
In general, there mostly exists so called shading which is a gentle slope of brightness value in the background. In most case, the brightness value of the object also has various values, and fluctuates. In these cases, it is difficult to detect a boundary line between the background and the object accurately over the whole areas by one kind of the threshold. Accordingly, in the present literature, the user establishes two kind thresholds. One threshold is capable of detecting the target object accurately and another threshold is capable of detecting the background area accurately. The user sets these two kind of thresholds interactively to be binarized, and in terms of the pixel having intermediate value in between the two thresholds, there is allocated the value corresponding to the intermediate value. Due to the use of this method, even though the boundary line is incapable of being obtained accurately, it is capable of obtaining the center of gravity location of the target object stably.
Another binarization method is described referring to the literature: xe2x80x9cImage Analysis Handbookxe2x80x9d (Supervision of M. Takagi, Y. Shimoda, Tokyo University Publication Party, pp502-505, 1991).
In the method introduced thus far, the user determined the threshold applied to whole area of picture in such away that the user set the threshold interactively while watching the image of the processing result. As shown in the literature, there is proposed a p-tile way, a method of Otu, or a method of Kittler by way of automatic determining method of the threshold. For instance, the method of Otu is so called as a discriminant analysis way in that on the assumption that a gray level histogram of an image is constituted by the sum of two normal distributions, this is the method for obtaining the threshold enabling them to be separated completely. At the same time, it is capable of calculating a degree of separation which is in use for measuring scale that it is capable of judging two distributions are separated to what degree, and that it is capable of judging whether or not bimodality of the histogram is high. Namely, the degree of separation is capable of being utilized by way of determination scale for determining whether or not the threshold being obtained is appropriate.
However, as described above, generally, the shading exists in the background mostly, accordingly there is a limit in the binarization method which sets the same threshold on the whole picture. Now, there is a method of a dynamic threshold processing for calculating the most suitable threshold in every pixel. According to the literature, the dynamic threshold processing is classified into two methods of a movement mean method and a sectional image division method.
The movement mean method is a simple method that when it causes brightness value of some pixel to be binarized, obtaining the mean value of the sectional image including the neighborhood thereof to be taken as the threshold.
The sectional image division method determines automatically the most suitable threshold in every respective sectional images while dividing the whole picture into a plurality of sectional images. The method causes the determined thresholds to be connected smoothly, thus constituting surface of the threshold over the whole picture so that the image is binarized.
There is described one example of the sectional image division method referring to the literature. Firstly, the image is divided into small area sectional images. FIG. 2 is a typical view. When the input image signal 1 shown in FIG. 1 is inputted, dividing the inputted image into sectional image signals 4, 5, 6, 7, 8 and so forth such that these sectional image signals overlap one another as shown in FIG. 2. Within the respective sectional image signals, the threshold and the degree of separation are calculated at this position while applying the binarization method of Otu. In the sectional image whose degree of separation comes into high rather than value set beforehand, since there is included both of the background and the target object, thus being judged that appropriate threshold is obtained. The appropriate threshold is adopted by way of the threshold value in the pixel of the center position of the sectional image, while in the another position of the pixel, the thresholds are connected smoothly, thus surface of the threshold of the whole picture is determined. By virtue of the above described procedure, it is capable of obtaining appropriate threshold over the whole picture, thus enabling suitable binarization result to be obtained even though there exists the shading or the like.
There are two problems in the chromakey technique that it should be prepared the background constituted by peculiar color beforehand and it is incapable of being used clothing and so forth whose color are identical with background color. The method of the literature: Japanese Patent Application Laid-Open No. HEI 2-206885 is incapable of being applied to the case where the target object has brightness value which bears a close resemblance to the background, because the target object should be taken photograph by way of the brightness value in which the target object is always brighter than the background or the target object is darker than the background. The method of the literature: Japanese Patent Publication No. HEI is not intended to obtain boundary in between the background and the target object accurately from the beginning, accordingly, it is incapable of utilizing in the case where accurate boundary line is required.
With respect to the method which applies one threshold to the whole picture, it is difficult to obtain satisfactory boundary line in most case, if shading exists in the background or in cases where brightness value of the object or color of the object is not of simplicity. As is referred to in the literature: Japanese Patent Publication No. HEI 4-116778, or in the literature: xe2x80x9cPixel Position Analysis Handbookxe2x80x9d.
The object of the movement mean method or the sectional image division method of the dynamic threshold processing is to apply to such the case. In the movement mean method or the sectional image division method of the dynamic threshold processing, it is necessary to set a presupposition that the background and the object which are constituted by one kind of brightness value or color in the position of the pixel should be taken photography. For this reason, it is incapable of applying the method to the case where the target object is constituted by a plurality of brightness values or colors. For instance, in the politics section or the sports section of the news paper, the photograph of the large number of persons faces appear therein while making them even so as to come to be the same position of faces and size with the identical background. When it causes such the space to be edited, it is necessary to remove original background precisely to contours because the origin of the photography of the face is taken photography with respective different background or different size. In most of the cases the background is even, however it is incapable of being estimated beforehand because the shading exists, the color of the hair or the skin, further clothes of the person are not even. In particular, it is difficult to separate between whitish background and a long-sleeved sport shirt, accordingly, it is extremely difficult to detect automatically the person image accurately to contours by the conventional method. Under the condition, a man of experience implements the work for removing the background by hand-work.
There is one object which is taken photograph under the background in which the shading exists uniformly, and which consists of single or a plurality of brightness or color. There is the other object which has brightness and color extremely nearly equal to the background. Namely, there is the problem that it is extremely difficult to detect such the objects precisely to contour with the exception of someone else.
In view of the foregoing, it is an object of the present invention to provide method and device of an object detectable and background removal and a storage medium for storing program which enable an object to be automatically detected to isolate precisely and accurately as far as the contours.
According to a first aspect of the present invention, for achieving the above-mentioned object, there is provided a method of an object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline comprising the steps of taking an image consisting both of a background and an object of detection target to be an input image by way of an input process of the image, selecting a sectional image including only the background while dividing the input image into the sectional image by way of a background only sectional image selection process, estimating a background on the input image based on a sectional image including the background by way of a background estimation process, and comparing the estimated background with the input image by way of a comparison process, wherein the method causes the object of detection target to be isolated from the input image.
In the first aspect of the present invention, a method of an object detectable and background removal is that a location of a pixel which is constituted by a background and an object of detection target is inputted, subsequently, a sectional image including only a background is selected while dividing the inputted location of the pixel into sectional images, then, estimating a background on an input image based on a sectional image including the background concerned, before comparing the estimated background with the above input image, thus isolating only an object of detection target.
Namely, in the first aspect of the present invention, a method of an object detectable and background removal firstly selects a sectional image including only a background while dividing image into sectional images, before estimating backgrounds in the whole images based on the sectional image concerned, thus enabling the background and unknown object for example, an object with brightness color distribution to be separated to detect accurately due to estimation of the background in the whole image based on the sectional image concerned.
In general, in order to separate accurately two things of a background and an object of detection target, it is necessary to find as precise as possible of a distribution of characteristic value such as brightness, color, edge and so forth which appear on image by way of a background or an object. In the first aspect of the present invention, a sectional image including only a background is selected, before a background in whole picture is estimated by using a technique of an interpolation and an extrapolation from the sectional image.
According to a second aspect of the present invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline comprising the steps of, taking an image consisting both of a virtually even background and an object of detection target to be an input image by way of an input process of the image, calculating a statistic in every respective sectional images while dividing the input image into sectional images by way of a statistic calculation process, selecting a sectional image including only the background based on the statistic calculated in the statistic calculation process by way of a background only sectional image selection process, estimating a statistic of the whole picture from a statistic of a sectional image including only the background by way of a statistic estimation process, determining a threshold in the whole picture from the estimated statistic by way of a threshold determination process, and comparing the threshold determined in the whole picture with the input image by way of a comparison process, wherein the method causes the object of detection target to be isolated from the input image.
In the second aspect of the present invention, an object detectable and background removal method calculates a statistic in every respective sectional images while dividing the input image into sectional images with an image constituted by virtually even background and an object of detection target as inputs, subsequently determining a threshold in the whole picture from the statistic calculate previously, so that it causes a detection target to be isolated while comparing the determined threshold in the whole picture with the above input image.
According to a third aspect of the present invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein said statistic calculation process consists of a sectional image division process dividing the input image into sectional images and a mean value and a standard deviation calculation process calculating a mean value and a standard deviation of a prescribed characteristic value of the divided sectional image.
In the second aspect of the invention, the object detectable and background removal method calculates a statistic in every respective sectional images while dividing the input image into sectional images, in the process of the second aspect concerned, in addition thereto, for instance, the object detectable and background removal method of a third aspect causes a mean value and a standard deviation of the brightness of a sectional image to be calculated while dividing the input image into sectional images.
Namely, in the third aspect of the present invention, an object detectable background removal method divides the input image into sectional images, subsequently, it is a characteristic of the method to calculate a mean value and a standard deviation using characteristic information of pixels within the respective sectional images. As shown in FIG. 1, when an input image signal 1 which is constituted from a background area 2 and a target object 3 is inputted to a device of the invention of the third aspect, firstly, dividing the input image signal 1 into sectional images 1-A, 1-B, . . . , 9-I as shown in FIG. 4. Then, it causes a mean value and a standard deviation of brightness and so forth to be calculated in a location of the sectional image in every respective sectional image signals. For instance, in FIG. 4, an area of sectional image 1-A is mentioned as below mark (1) with number of row and column arranged.
C1-Axe2x80x83xe2x80x83(1) 
A brightness value for instance, a spot of coordinates (x, y) is mentioned as following (2).
Ix,yxe2x80x83xe2x80x83(2) 
At this time, for instance, a mean value of the brightness in the sectional image i-p is defined with equation (3). Here, a denominator of the equation (3) is an area of the sectional image.                               μ                      i            -            p                          =                              (                                          ∑                                  x                  ,                  yε                                            ⁢                                                C                                      i                    -                    p                                                  ⁢                                  I                                      x                    ,                    y                                                                        )                    /                      (                                          ∑                                  x                  ,                  yε                                            ⁢                                                C                                      i                    -                    p                                                  ⁢                1                                      )                                              (        3        )            
A standard deviation is defined with a equation (4) utilizing the mean value.                               σ                      i            -            p                          =                              √                          {                                                ∑                                      x                    ,                    yε                                                  ⁢                                                                            C                                              i                        -                        p                                                              ⁡                                          (                                                                        I                                                      x                            ,                            y                                                                          -                                                  μ                                                      i                            -                            p                                                                                              )                                                        2                                            }                                /                      {                                          ∑                                  x                  ,                  yε                                            ⁢                                                C                                      i                    -                    p                                                  ⁢                1                                      }                                              (        4        )            
The mean value and the standard deviation are calculated over the whole sectional images based on the equations (3), and (4).
Further, in the third aspect of the present invention, a geometrical mean or a harmonic mean or median or the like which are described in the literature: xe2x80x9cModern Mathematical Science Dictionaryxe2x80x9d published by Maruzen Co., Ltd, 1991 pp. 495 are capable of being used instead of an arithmetical mean shown in the above equation (3). Similarly, a statistic of an absolute deviation or a quarter deviation or the like are capable of being used instead of the standard deviation shown in the equation (4) or a distribution represented by square thereof.
Furthermore, as shown in FIG. 4, the sectional images are arranged in tile-shaped configuration, however as shown in FIG. 2, the sectional images are superimposed with each other in part thereof, or the sectional images are arranged while leaving a space therebetween. All of these configurations are of effectiveness by way of the third aspect of the present invention.
According to fourth aspect of the present invention, there is provided a method of an object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process judges a sectional image whose standard deviation of the prescribed characteristic value is of the smallest value as a sectional image whose probability of including only a background is high, and judges a sectional image whose difference of comparison is less than prescribed threshold to be a sectional image including only the background while comparing a standard deviation of the prescribed characteristic value in another sectional image with a sectional image whose probability of including only the background is high.
In the third aspect of the invention, the object detectable and background removal method selects the sectional image including only the background based on the calculated statistic, in the process of the third aspect concerned, in addition thereto, the object detectable and background removal method of the fourth aspect judges a sectional image whose standard deviation of a characteristic value such as brightness and so forth is of the smallest value to be a sectional image whose probability of including only a background is high, subsequently, comparing for instance, a standard deviation of the brightness in the another sectional image with a standard deviation of the sectional image whose probability of including the background is high, thus judging a sectional image whose difference of the standard deviations therebetween is less than the threshold to be a sectional image including only a background.
Namely, in the fourth aspect, when the method causes a sectional image including only a background to be selected from sectional images, for instance, selecting a sectional image whose standard deviation of the brightness is of the most smallest value, subsequently, selecting sectional images whose standard deviations bear resemblance to those of the sectional image selected previously to be sectional images including only backgrounds. On the images inputted thereto, since backgrounds have virtually even distribution, it is capable of being expected that a sectional image including only a background has a small standard deviation. For this reason, a sectional image whose probability of including only a background is obtained due to a condition that a standard deviation is of the most smallest value. For instance, in FIG. 4, the standard deviation of the sectional image 1-A is of the most smallest value, thus the sectional image 1-A is judged to be a sectional image whose probability of including only a background is high. The standard deviation of the sectional image 1-A is set to "sgr"bg. There is implemented a judgement whether or not another sectional image i-p includes only a background in such a way that it is judged due to a threshold processing of a equation (5) using a standard deviation of the sectional image i-p, the standard deviation of the sectional image 1-A, and two constants xcex7.                               ησ          -                      σ            bg                          ≦                  σ                      i            -            p                          ≦                  ησ          +                      σ            bg                                              (        5        )            
Here, xcex7"sgr"xe2x88x92 is a constant given beforehand which is more than 0 and until 1, further, xcex7"sgr"+ is a constant value given beforehand which is more than 1.
For instance, with respect to a mean value of the brightness, when shading is given on a background, even though a sectional image includes only a background, a mean value of the brightness is of the different value according to a location on an image, thus the mean value of the brightness does not qualify for the judgement whether or not a sectional image includes only a background. The equation (5) is described by way of one example of a threshold processing, however, for instance, a equation (6) by using constant xcex4 to be more than 0 and so forth are capable of constituting effective invention in conformity with a target by defining variously in terms of the threshold processing.                                           σ            bg                    -          δσ          -                ≦                  σ                      i            -            p                          ≦                              σ            bg                    +          δσ          +                                    (        6        )            
FIG. 5 is an example of selection result of a sectional image including only a background while giving light hatching, thus showing simultaneously an object of detection target 3. A group of sectional images which is image with the exception of an object of detection target 3 and which is not influenced by noise largely is selected to be sectional images including only a background.
According to a fifth aspect of the present invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process selects sectional images in such a way that it causes the sectional images of the specified number in order of the smaller number of a standard deviation of the prescribed characteristic value to be selected, thus taking such sectional images to be the sectional image whose probability of including only the background is high.
In the fourth aspect of the present invention, the object detectable and background removal method selects the sectional image whose probability of including only a background is high, in the process of the fourth aspect concerned, in addition thereto, the object detectable and background removal method of the fifth aspect selects sectional images in answer to the specified numbers in order of smaller value of a standard deviation of the brightness to be a sectional image whose probability of including only a background is high.
According to a sixth aspect of the present invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process selects sectional images in such a way that it causes the sectional image having the most nearest value to the standard deviation of the prescribed characteristic value instructed beforehand to be selected by way of the sectional image whose prescribed characteristic value of including only the background is high.
In the fourth aspect of the invention, the object detectable and background removal method selects a sectional image whose probability of including only a background is high, in the process of the fourth aspect concerned, in addition thereto, the object detectable and background removal method of the sixth aspect selects a sectional image whose standard deviation is of the most nearest value of the standard deviation of the brightness to be a sectional image whose probability of including only a background is high.
According to a seventh aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process selects sectional images in such a way that it causes the sectional image by the number specified in order of the nearer number to the standard deviation of the prescribed characteristic value instructed beforehand to be selected by way of the sectional image whose prescribed characteristic value of including only the background is high.
In the fourth aspect of the invention, the object detectable and background removal method selects a sectional image whose probability of including only a background is high, in the process of the fourth aspect concerned, in addition thereto, the object detectable and background removal method of the seventh aspect selects sectional images in answer to the specified numbers in order of nearer value of standard deviation of the brightness instructed beforehand to be a sectional image whose probability of including only a background is high.
According to an eighth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process selects sectional images in such a way that it causes the sectional image having the most nearest value both to the mean value and the standard deviation of the prescribed characteristic value instructed beforehand to be selected by way of the sectional image whose probability of including only the background is high.
In the fourth aspect of the invention, the object detectable and background removal method selects a sectional image whose probability of including only a background is high, in the process of the fourth aspect concerned, in addition thereto, the object detectable and background removal method of the eighth aspect selects a sectional image whose mean value and standard deviation are the most nearest value of the mean value and the standard deviation of the brightness instructed beforehand to be a sectional image whose probability of including only a background is high.
Namely, the eighth aspect of the invention specifies for instance, not only a standard deviation of the brightness but also a mean value thereof beforehand when selecting a sectional image whose probability of including only a background is high in comparison with the seventh aspect. If shading is given on a background, it is difficult to utilize a mean value of the brightness and so forth, lastly when selecting a sectional image including only a background. However, when there is selected a sectional image whose probability of including only a background coming to be a criterion of the selection, namely, when selecting a sectional image including only a typical background, the eighth aspect comes to be an effective invention in conformity with a target.
According to a ninth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process selects sectional images in such a way that it causes the sectional images of the number specified in order of the nearer value to the mean value and the standard deviation of the prescribed characteristic value instructed beforehand to be selected by way of the sectional image whose probability of including only the background.
In the fourth aspect of the invention, the object detectable and background removal method selects the sectional image whose probability of including a background, in the process of the fourth aspect concerned, in addition thereto, the object detectable and background removal method of the ninth aspect for instance, selects sectional images in answer to the specified number in order of nearer value both of a mean value and a standard deviation of the brightness instructed beforehand to be a sectional image whose probability of including a background.
According to a tenth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process causes the sectional image whose probability of including only the background is high to be either single image or a plurality of images instructed beforehand.
In the fourth aspect of the invention, the object detectable and background removal method selects the sectional image whose probability of including only the background is high, in the process of the fourth aspect concerned, in addition thereto, the object detectable and background removal method of the tenth aspect judges a partial image whose probability of including only a background is high to be either single sectional image or a plurality of sectional images instructed beforehand.
Namely, in the invention of the tenth aspect, there is instructed beforehand a partial image whose probability of including only a background is high. For instance, as shown in FIG. 1, when an image is taken photograph while controlling so as to locate an object in the center of image, in this case, it is certain that four corners of the image of the sectional images 1-A, 9-A, 1-I, 9-I in FIG. 4 are backgrounds. In another case of photograph of person appears in a news paper, both corners located above sectional images 1-A, and 9-A are backgrounds surely in FIG. 4. As described above, the tenth aspect enables another sectional images including only a background to be selected based on the sectional images including surely only a background.
According to an eleventh aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process selects a sectional image whose probability of including only the background is high from sectional images included in an area instructed beforehand.
In the fourth to ninth aspects of the invention, the object detectable and background removal method selects the sectional image whose probability of including only the background is high, in the process of the fourth aspect to ninth aspect concerned, in addition thereto, when the object detectable and background removal method of the eleventh aspect selects a sectional image whose probability of including only a background is high, selecting it from a predicted area of including only a background beforehand instead of the whole sectional images.
According to a twelfth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process selects a sectional image whose probability of including only the background is high from sectional images included in a plurality of areas instructed beforehand.
In the fourth aspect to the ninth aspect of the invention, the object detectable and background removal method selects the sectional image whose probability of including only the background is high, in the process of the fourth aspect to ninth aspect concerned, in addition thereto when the object detectable and background removal method of the twelfth aspect selects a sectional image whose probability of including only a background is high, from sectional images involved in a plurality of areas instructed beforehand.
According to a thirteenth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein said statistic calculation process consists of a sectional image division process dividing the input image into sectional images, and a mean value and standard deviation calculation process calculating a skewness from a mean value and standard deviation of a prescribed characteristic value of a sectional image.
In the second aspect of the invention, the object detectable and background removal method calculates the statistic in every respective sectional images while dividing the input image into sectional images, in the process of the second aspect concerned, in addition thereto, the object detectable and background removal method of the thirteenth aspect for instance, calculates a mean value, a standard deviation, and a skewness of the brightness of a sectional image while dividing an input image into sectional images.
Namely, the invention of the thirteenth aspect newly calculates the skewness in comparison with the third aspect. The skewness of the sectional image i-p is defined based on the literature: xe2x80x9cMathematical Statisticsxe2x80x9d written by Takashi Takeuchi, published by Toyo Keizai, 1963, pp. 29, by a following equation (7).                               ζ                      i            -            p                          =                              (                          1              /                              σ                                  i                  -                  p                                3                                      )                    ⁢                      {                                          ∑                                  x                  ,                  yε                                            ⁢                                                                                          C                                              i                        -                        p                                                              ⁡                                          (                                                                        I                                                      x                            ,                            y                                                                          -                                                  μ                                                      i                            -                            p                                                                                              )                                                        3                                /                                                      ∑                                          x                      ,                      yε                                                        ⁢                                                            C                                              i                        -                        p                                                              ⁢                    1                                                                        }                                              (        7        )            
As described in the same literature xe2x80x9cMathematical Statisticsxe2x80x9d pp.29, a skewness is quantity used for representing slippage from normal distribution, and also is a standard judging lateral symmetric property of distribution. On the assumption that a background is virtually even state in a sectional image, it is capable of being understood that for instance, brightness distribution is like a normal distribution, thereby, the skewness is of usefulness by way of judgement material whether or not an image is a background. The skewness becomes zero when the distribution concerned agrees with the normal distribution, the skewness take a separated value from zero when the distribution concerned gets out of the normal distribution.
In the thirteenth aspect of the invention, a kurtosis which is described in the same literature is capable of being used instead of the skewness. The kurtosis is available quantity by way of judgement condition whether or not an image is a background, and is available standard of simplicity of the brightness distribution.
According to a fourteenth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process judges a sectional image whose standard deviation of the prescribed characteristic value is of the most smallest one as a sectional image whose probability of including only the background is high among sectional images whose absolute value of skewness of the prescribed characteristic value is less than a threshold, while in another sectional images, the sectional image selection process judges a sectional image whose absolute value of the skewness of the prescribed characteristic value is less than the threshold and whose difference of a standard deviation of the prescribed characteristic value between the sectional image whose probability of including only the background is high and the sectional image concerned is less than the threshold as a sectional image including only the background.
In the thirteenth aspect of the invention, the object detectable and background removal method selects the sectional image including only the background based on the calculated statistics, in the above process of the thirteenth aspect concerned, in addition thereto, the object detectable and background removal method of the fourteenth aspect judges a sectional image whose standard deviation of the brightness is of the most smallest value to be a sectional image whose probability of including only a background is high among sectional images whose absolute value of the skewness of the brightness and so forth is less than the threshold, in another sectional images, for instance, the fourteenth aspect judges a sectional image whose absolute value of the skewness of the brightness and so forth is less than the threshold and whose difference of standard deviation between the sectional image concerned and the sectional image whose probability of including only the background is high is less than the threshold to be a sectional image including only a background.
In the invention of the fourteenth aspect, the skewness is utilized when judging whether or not a sectional image includes only a background. For instance, there can be realized a threshold processing whether or not the sectional image includes a background by a equation (8).                               "LeftBracketingBar"                      ζ                          i              -              p                                "RightBracketingBar"                 less than         ηζ                            (        8        )            
Here, xcex7xcex6 is a constant determined beforehand which takes larger value more than zero (0). According to judgement of the present skewness, it enables a distribution maybe background whose brightness value distribution is virtually even to be investigated within sectional images.
According to a fifteenth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process judges a sectional image whose absolute value of a skewness of the prescribed characteristic value is less than the threshold as a sectional image whose probability of including only the background is high.
In the fourteenth aspect of the invention, the object detectable and background removal method selects the sectional image whose probability of including only the background is high, in the process of the fourteenth aspect concerned, in addition thereto, the object detectable and background removal method of the fifteenth aspect for instance, judges a sectional image whose absolute value of the skewness of the brightness is less than the threshold to be a sectional images whose probability of including only a background is high.
Namely, the invention of the fifteenth aspect investigates only a distribution to be a background whose skewness is of the small value and whose brightness value distribution is approximately uniform in the sectional images.
According to a sixteenth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process selects sectional images of the number specified in order of smaller standard deviation among sectional images whose absolute value of a skewness of the prescribed characteristic value is less than the threshold, subsequently judging the sectional image concerned as a sectional image whose probability of including only the background is high.
In the fourteenth aspect of the invention, the object detectable and background removal method selects the sectional image whose probability of including only the background is high, in the process of the fourteenth aspect concerned, in addition thereto, the object detectable and background removal method of the sixteenth aspect for instance, selects sectional images in answer to the specified numbers in order of smaller value of the standard deviation among sectional images whose absolute value of the skewness of the brightness and so forth is less than the threshold to be a sectional image whose probability of including only a background is high.
According to a seventeenth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process selects sectional images of the number specified in order of smaller standard deviation among sectional images whose absolute value of a skewness of the prescribed characteristic value is less than the threshold, subsequently judging the sectional image concerned as a partial image whose probability of including only the background is high.
In the fourteenth aspect of the invention, the object detectable and background removal method selects the sectional image whose probability of including only the background is high, in the process of the fourteenth aspect concerned, in addition thereto, the object detectable and background removal method of the seventeenth aspect for instance, selects a sectional image whose standard deviation is of the most nearest value of the standard deviation of the brightness instructed beforehand among sectional images whose absolute value of the skewness of the brightness and so forth is less than the threshold, thus judging to be a sectional image whose probability of including only a background is high.
According to a eighteenth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process selects sectional images as many as the number specified in order of the most nearest value to the standard deviation of the skewness of the prescribed characteristic value instructed beforehand among sectional images whose absolute value of skewness of the prescribed characteristic value is less than the threshold, to judge the sectional image concerned as the sectional image whose probability of including only the background is high.
In the fourteenth aspect of the invention, the object detectable and background removal method selects the sectional image whose probability of including only the background is high, in the process of the fourteenth aspect concerned, in addition thereto, the object detectable and background removal method of the eighteenth aspect for instance, selects sectional images in answer to the specified number in order of nearer value of the standard deviation of the brightness instructed beforehand among sectional images whose absolute value of the skewness of the brightness and so forth is less than the threshold, thus judging to be a sectional image whose probability of including only a background is high.
According to a nineteenth aspect of the invention, there is a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process selects a partial image whose standard deviation is of the most nearest value to the standard deviation of the prescribed characteristic value instructed beforehand among sectional images whose absolute value of the skewness of the prescribed characteristic value is less than the threshold, thus judging the sectional image concerned as the sectional image whose probability of including only the background is high.
In the fourteenth aspect of the invention, the object detectable and background removal method selects the sectional image whose probability of including only the background is high, in the process of the fourteenth aspect concerned, in addition thereto, the object detectable and background removal method of the nineteenth aspect for instance, selects sectional images in answer to the specified number in order of nearer value of the mean value and the standard deviation of the brightness instructed beforehand among sectional images whose absolute value of the skewness of the brightness and so forth is less than the threshold, thus judging to be a sectional image whose probability of including only a background is high.
According to a twentieth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process selects partial images as many as the number specified in order of the most nearest value to a mean value and a standard deviation of the prescribed characteristic value instructed beforehand among sectional images whose absolute value of the skewness of the prescribed characteristic value is less than the threshold, thus judging the sectional image concerned as the sectional image whose probability of including only the background is high.
In the fourteenth aspect of the invention, the object detectable and background removal method selects the sectional image whose probability of including only the background is high, in the process of the fourteenth aspect concerned, in addition thereto, the object detectable and background removal method of the twentieth aspect for instance, selects sectional images in answer to the specified number in order of nearer value of the standard deviation of the brightness and so forth instructed beforehand among sectional images whose absolute value of the skewness of the brightness is less than the threshold, thus judging to be a sectional image whose probability of including only a background is high.
According to a twenty first aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process to which there is given beforehand a probability of including only the background in every sectional image involved in a plurality of areas instructed beforehand, judges a sectional image whose probability of including only the background is the most highest as the sectional image whose probability of including only the background is high among sectional images whose absolute value of the skewness of prescribed characteristic value is less than the threshold.
In the fourteenth aspect of the invention the object detectable and background removal method thereof selects the sectional image whose probability of including only the background is high, in the process of the fourteenth aspect concerned, in addition thereto, the object detectable and background removal method of the twenty first aspect to which for instance, the probability of including only the background is given beforehand in every sectional images involved in the area instructed beforehand, thus judging a partial image whose probability of including only a background is high to be the sectional image whose probability of including only the background is high among sectional images whose absolute value of the skewness of the brightness is less than the threshold.
Namely, the invention of the twenty first aspect selects the sectional image whose probability of including only the background given beforehand is of the most highest value among sectional images which satisfy the condition that the absolute value of the skewness is less than the threshold. For instance, as shown in FIG. 1, when there is intended to locate an object to the center position, the probability of including only a background in the corners of the image is high rather than the center position. There, firstly, there is given a probability of including only the background to respective sectional images beforehand. For instance, in FIG. 6, there is established beforehand the probability of including only the background of the sectional images 1-A, 2-A, 1-B, and 2-B to 0.8, 0.6, 0.4, and 0.2 respectively, and 0 (zero) is established to another sectional images. There is judged in the twenty first aspect that the sectional image whose probability of including only the background is of the most highest value with the exception of the value of zero and whose absolute value of the skewness of respective sectional images is less than the threshold, to be the sectional image whose probability of including only the background is high. By virtue of the matter described above, there can be selected the sectional image whose higher probability of including only the background on the location of the image while giving it preference.
According to a twenty second aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image selection process to which there is given beforehand a probability of including only the background respectively in every sectional image involved in a plurality of areas instructed beforehand, selects the sectional image whose probability of including only the background is of the highest one from respective areas among sectional images whose absolute value of the skewness of the prescribed characteristic value is less than the threshold, thus judging the sectional image concerned as the sectional image whose probability of including only the background is high.
In the fourteenth aspect of the invention, the object detectable and background removal method selects the sectional image whose probability of including only the background is high, in the process of the fourteenth aspect concerned, in addition thereto, the object detectable and background removal method of the twenty second aspect to which the respective probabilities of including only the background are given in every sectional image involved in a plurality of areas instructed beforehand, selects a sectional image whose probability of including only a background is of the most highest value from respective areas among sectional images whose absolute value of the skewness of the brightness is less than the threshold, thus judging to be a sectional image whose probability of including only the background is high.
Namely, the invention of the twenty second aspect establishes a plurality of group of sectional images to which the probabilities are given in comparison with the twenty first aspect. On account of this matter, it enables the sectional image to be selected while establishing the probabilities of including only the background to four corners of the image independently.
According to a twenty third aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the sectional image whose probability of including only the background is high is selected from the sectional image involved in at least one area instructed beforehand in the sectional image selection process.
In the fourteenth aspect to the twenties aspect of the invention, the object detectable and background removal method selects the sectional image whose probability of including only the background is high, in the process of the fourteenth aspect to the twenties aspect concerned, in addition thereto, the object detectable and background removal method of the twenty third aspect selects a sectional image whose probability of including only a background is high from a sectional image involved in at least one area instructed beforehand.
According to a twenty fourth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the statistic estimation process estimates a mean value and a standard deviation of the prescribed characteristic value over the whole picture while utilizing the mean value and the standard deviation of the prescribed characteristic value of the sectional image including only the background.
In the second aspect to the twenty third aspect of the invention, the object detectable and background removal method estimates the statistics in the whole picture from the sectional image including only the background, in the process of the second aspect to the twenty third aspect concerned, in addition thereto, the object detectable and background removal method of the twenty fourth aspect estimates a mean value and a standard deviation of the brightness and so forth by way of the background over the whole picture while utilizing for example, the mean value and the standard deviation of the brightness of the sectional image including only the background.
Namely, the invention of the twenty fourth aspect, since the mean value and the standard deviation of the brightness and so forth of the sectional image including only the background selected previously are the mean value and the standard deviation of the brightness and so forth of the background of the location of the sectional image concerned, thus estimating a mean value and a standard deviation of the brightness and so forth of a background over the whole picture due to the fact that the mean value and the standard deviation in the location of the sectional image are subjected to the interpolation and the extrapolation.
According to a twenty fifth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein when the statistics estimation process estimates a mean value and a standard deviation of the prescribed characteristic value by way of the background in the sectional image including image with the exception of the background, if there exists the mean value and the standard deviation of the prescribed characteristic value of the sectional image including only the background located in the neighborhood and the mean value and the standard deviation of the prescribed characteristic value of the sectional image including image with the exception of the background estimated previously in the same neighborhood, thus estimating the mean value and the standard deviation by averaging above respective average values and the standard deviations, while if there does not exist them, estimating the mean value and the standard deviation of the prescribed characteristic value in another sectional images including image with the exception of the background, thus repeating estimation processing both of a mean value and a standard deviation of the prescribed characteristic value of the background until when it is capable of estimating a mean value and a standard deviation of the prescribed characteristic value of the background in the whole sectional images including image with the exception of the background.
In the twenty third aspect of the invention, the object detectable and background removal method estimates the statistics in the whole picture from the partial image including only the background, in the process of the twenty third aspect concerned, in addition thereto, in the object detectable and background removal method of the twenty fifth aspect, when the statistics estimation process estimates for instance, a mean value and a standard deviation of the brightness by way of the background in the sectional image including image with the exception of the background, if there exists the mean value and the standard deviation of the brightness of the sectional image including only the background located in the neighborhood and the mean value and the standard deviation of the brightness of the sectional image including image with the exception of the background estimated previously in the same neighborhood, thus estimating the mean value and the standard deviation by averaging above respective average values and the standard deviations, while if there does not exist them, estimating the mean value and the standard deviation of the brightness in another sectional images including image with the exception of the background, thus repeating estimation processing both of a mean value and a standard deviation of the brightness of the background until when it is capable of estimating a mean value and a standard deviation of the brightness of the background in the whole sectional images including image with the exception of the background.
Namely, the invention of the twenty fifth aspect estimates a mean value and a standard deviation of the brightness and so forth which the background would have, in the location of the sectional image including image with the exception of the background. For instance, in FIG. 5, when there is paid attention to the sectional image 2-A judged as the sectional image including image except the background, three of 1-A, 3-A, and 2-B within the sectional images of the neighborhood having the side in common are judged as the sectional image including only the background. Consequently, it can be understood that the mean value and the standard deviation of the brightness of the background in the location of the sectional image 2-A analogize with those of the above sectional images 1-A, 3-A, and 2-B, therefore, a mean value and a standard deviation of the brightness of the sectional image 2-A can be estimated by a equation (9) based on the mean value and the standard deviation of the brightness of the sectional images 1-A, 3-A, and 2-B.                                           μ                          2              -              A                                =                                    (                                                μ                                      1                    -                    A                                                  +                                  μ                                      3                    -                    A                                                  +                                  μ                                      2                    -                    B                                                              )                        /            3                          ⁢                  
                ⁢                              σ                          2              -              A                                =                                    (                                                σ                                      1                    -                    A                                                  +                                  σ                                      3                    -                    A                                                  +                                  σ                                      2                    -                    B                                                              )                        /            3                                              (        9        )            
Similarly, in FIG. 5, the sectional image 2-D which is judged as the partial image including image except the background, a mean value and a standard deviation thereof is estimated from the mean value and the standard deviation of the partial images 2-C, and 1-A which are judged as the sectional image including only the background. As described above, only the sectional image having a judged sectional image of including only the background in the neighborhood thereof among sectional images judged as image including image except the background, to which the estimation of a mean value and a standard deviation of the brightness by way of the background is implemented. The sectional image obtaining the mean value and the standard deviation by the present estimation is dealt with by way of the sectional image including only the background thereafter, thus being utilized in estimation of another sectional image judged as image including image except the background. It is capable of estimating a mean value and a standard deviation of the brightness by way of the background over the whole sectional images due to repeating of the present operation.
Namely, in the neighborhood of the sectional image 1-p, when there is only one sectional image whose mean value and standard deviation of the brightness and so forth by way of the background are estimated and whose number of the sectional image is set to be j-q, a mean value and a standard deviation of the brightness and so forth by way of a background in the sectional image i-p are estimated by a equation (10).
xcexci-p=xcexcj-q 
"sgr"i-p="sgr"j-qxe2x80x83xe2x80x83(10) 
Similarly, when the mean value and the standard deviation of the brightness are estimated in the neighborhood of the two sectional images j-q and k-r, a mean value and a standard deviation of the brightness and so forth by way of a background in the sectional image i-p are estimated by a equation (11).
xcexci-p=(xcexcj-q+xcexck-r)/2 
"sgr"i-p=("sgr"j-q+"sgr"k-r)/2xe2x80x83xe2x80x83(11) 
Further, when the mean value and the standard deviation of the brightness are estimated in the neighborhood of the three sectional images j-q, k-r, and l-s, a mean value and a standard deviation of the brightness and so forth by way of a background in the sectional image i-p are estimated by a equation (12).
xcexci-p=(xcexcj-q+xcexck-r+xcexcl-s)/3 
"sgr"i-p=("sgr"j-q+"sgr"k-r+"sgr"l-s)/3xe2x80x83xe2x80x83(12) 
And so forth, it is capable of being defined along the number of the sectional images in the neighborhood thereof.
In the above estimation processing, there is defined the sectional image having the side in common in regard to the sectional image by way of the target, to be a neighborhood, in addition thereto, it is capable of defining neighborhood of including sectional image having the vertexes in common, and including a sectional image whose distance is long, as the neighborhood, thus it is capable of implementing the same estimation processing as above. Further, the equations (10), (11), and (12) for estimating a mean value and a standard deviation are simple average, however, it is capable of being utilized the complicated equations such as a equation taking a center value, a equation weighted-averaging by distance of sectional images therebetween, and a equation using a quadratic average, or a cubic average.
According to a twenty sixth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, the statistic estimation process calculates location of the center of gravity of the sectional image including image with the exception of the background to be taken as the location of the center of gravity of an object, and calculating all of distance between respective sectional images and the location of the center of gravity of the object, when the statistic estimation process estimates a mean value and a standard deviation of the prescribed characteristic value by way of the background in the sectional images of including image with the exception of the background, in case where there exists only one set of a mean value and a standard deviation of the sectional image which is located far off more than the distance between the sectional image including images with the exception of the background and the location of the center of gravity, and which is located in the neighborhood thereof including only the background, and which is located at the same as above including images with the exception of the background estimated previously, thus estimating the mean value and the standard deviation concerned by obtaining a mean value thereof, while in case where there does not exists only one set of a mean value and a standard deviation of the sectional image, estimating a mean value and a standard deviation of the prescribed characteristic value in another sectional images including images with exception of the background, thus repeating an estimating processing of a mean value and a standard deviation of the prescribed characteristic value of the background until when it is capable of estimating a mean value and a standard deviation of the prescribed characteristic value of a background in the whole sectional images including images with the exception of the background.
In the twenty fourth aspect of the invention, the object detectable and background removal method estimates the statistics in the whole picture from the sectional image including only the background, in the process of the twenty fourth aspect concerned, in addition thereto, in the object detectable and background removal method of the twenty sixth aspect, the statistic estimation process calculates location of the center of gravity of the sectional image including image with the exception of the background to be taken as the location of the center of gravity of an object, and calculating all of distance between respective sectional images and the location of the center of gravity of the object, when the statistic estimation process estimates a mean value and a standard deviation of the brightness by way of the background in the sectional images of including image with the exception of the background, in case where there exists only one set of a mean value and a standard deviation of the sectional image which is located far off more than the distance between the sectional image including images with the exception of the background and the location of the center of gravity, and which is located in the neighborhood thereof including only the background, and which is located at the same as above including images with the exception of the background estimated previously, thus estimating the mean value and the standard deviation concerned by obtaining a mean value thereof, while in case where there does not exists only one set of a mean value and a standard deviation of the sectional image, estimating a mean value and a standard deviation of the brightness in another sectional images including images with exception of the background, thus repeating an estimating processing of a mean value and a standard deviation of the brightness of the background until when it is capable of estimating a mean value and a standard deviation of the brightness of a background in the whole sectional images except the background.
Namely, the invention of the twenty six aspect obtains a location of the center of gravity of an object to be a detection target while calculating the location of the center of gravity of the sectional image except the background in comparison with the twenty fifth aspect, thus estimating a mean value and a standard deviation of the brightness by way of the background of the sectional image except the background by using only the mean value and the standard deviation of the brightness by way of the background in the sectional image farther away from the location of the center of gravity among the sectional images including only the background in the neighborhood thereof.
For instance, there is supposed that a brightness distribution of a sectional image analogize with that of another sectional image on the inside of background and an object of detection target accidentally. In this case, the processing of an interpolation/extrapolation to the sectional image of the neighborhood as the invention of the twenty fifth aspect can not estimate correctly the mean value of the brightness by way of the background at the intermediate location between the sectional image at the location of background and the sectional image within the object. The invention of the twenty sixth aspect can estimate a mean value of the brightness by way of the background correctly in this case using FIG. 6.
As shown in FIG. 6, the brightness distribution of the background area 2 analogize with that of inside of the object 10, thus the sectional image with the judgement of including only the background appears at both of the background area 2 and the inside of the object 10. However, the sectional image which passes an object-contours 9 is judged as a sectional image except the background because the standard deviation or the skewness becomes large. In FIG. 6, the sectional image with light hatching is the sectional image judged that only the background is included. When there is obtained the mean value of the brightness by way of the background at the location of the sectional image 11 existing on the object-contours 9 in accordance with the twenty fifth aspect from only the sectional images 12, 13, 18, and 19 which are judged that only the background and exists in the neighborhood thereof, there is obtained a wrong intermediate value of the background area 2 and the inside of the object 10.
The twenty sixth aspect can handle such the case because there is calculated the center of gravity of the sectional image judged as except the background to be obtained the location of the center of gravity. The twenty sixth aspect compares the distance between the sectional images 12 to 19 in the neighborhood thereof and an object center location 20 to be rough location of the object with the distance between the watched sectional image 11 and the object center of gravity location 20, subsequently, setting only the sectional images 12, and 13 located farther away therefrom to be candidates of calculation, thus eliminating influence of the sectional image wrongly judged as the background regardless of existence within the object 10. Since the sectional image which passes the object-contours 9 is judged as except the background, the location of the center of gravity viewing from the watched sectional image 11 can not be obtained accurately. However there is no influence.
The influence of the sectional image wrongly judged as only background in the inside of the object remains in the background estimated previously. When there is intended to detect an object based on the background estimated previously thereafter, enabling the influence to be eliminated easily by taking account of connectivity to the sectional image whose probability of including only the background.
According to a twenty seventh aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the statistic estimation process is provided with respective peculiar neighborhood relationships in terms of respective sectional images on an image.
In the twenty fifth aspect of the invention, the object detectable and background removal method estimates the statistics in the whole picture from the sectional image including only the background, in the process of the twenty fifth aspect concerned, in addition thereto, the twenty seventh aspect to which respective peculiar neighborhood relationships are given in terms of the respective sectional images on the image.
Namely, the invention of the twenty seventh aspect does not use the object center of gravity location as described in the twenty sixth aspect, but the neighborhood relationship of the sectional image is set in every respective sectional images of the image beforehand. FIG. 7 is an explanation view showing the neighborhood relationship. When there is estimated a mean value and a standard deviation of the brightness by way of the background in the sectional image 11, thus determining beforehand that which of the sectional images 12 to 19 having the side and the apexes in common is defined as the neighborhood.
As shown in FIG. 8, when the detection target object 3 is taken the photograph at the left corner of the image, it does not necessarily need the location of the center of gravity. When there is estimated the mean value and the standard deviation of the brightness by way of the background in the sectional image 11 in FIG. 8, defining the sectional images 13, 14, and 16 in FIG. 7 to be the neighborhood. On account of this matter, it is capable of being used only the statistics of the sectional image located in the direction of apparently the background, and it is capable of obtaining object contours accurately.
Similarly, as shown in FIG. 9, there is supposed that the detection target object 3 exists beforehand below the center of the picture. When there is taken notice of the sectional image 11 in FIG. 9, there does not occur inversion of relationship of the location between the inside of object and the background because only the sectional images 12, 13, and 15 in FIG. 7 at the left side area of the boundary line 21 are treated as the neighborhood, and because only the sectional images 13, 14, and 16 in FIG. 7 at the right side area of the boundary line 21 are treated as the neighborhood, thus there does not occur wrong estimation in the neighborhood of the boundary. In the person photographs, similar composition is in use in lot of cases, the twenty seventh aspect is effective.
According to a twenty eighth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the statistic estimation process judges the mean value and the standard deviation of the prescribed characteristic value in the sectional images including only the background as the mean value and the standard deviation of the prescribed characteristic value of the background in the location of a center pixel of the sectional image, when estimating a mean value and a standard deviation of the prescribed characteristic value by way of the background in respective pixels on the image, calculating whole distances between the location of the pixel and the center pixel of the sectional image including only the background, thus estimating a mean value and a standard deviation of the prescribed characteristic value of the background in the location of the pixel, due to the fact that the mean value and standard deviation of the prescribed characteristic value in the location of center pixel of the sectional image including only the background are weighted in answer to the corresponding distance.
In the twenty fifth aspect to the twenty seventh aspect of the invention, the object detectable and background removal method estimates the statistics in the whole picture from the sectional image, however, the object detectable and background removal method of the twenty eighth aspect implements an estimation in every pixel. The invention of the twenty eighth aspect is explained using FIGS. 5 and 10. In FIG. 5, the sectional image with light hatching includes only the background, thus a set of these partial images is taken to be "psgr". Further, a center pixel location of the sectional image group is specified by c1-A, c1-B, . . . , c9-I and column and row. The mean value and the standard deviation of the brightness by way of the background in the center pixel location agree with the mean value and the standard deviation of the brightness of the sectional image concerned.
When there is estimated the mean value and the standard deviation of the brightness by way of the background in the estimated pixel location 22, calculating the distance between the present estimated pixel location 22 and the center pixel location in the set "psgr" of the sectional image including only the background respectively. Here, the coordinates location of the estimated pixel location 22 is set to be (xq, yq), and the center coordinates location ci-p of the sectional image i-p including only the background is set to be (xci-p, yci-p), thus the distance dq,ci-p of the two points therebetween is calculated a equation (13).                               d                      q            ,                          ci              -              p                                      =                  √                      {                                                            (                                                            x                      q                                        -                                          x                                              ci                        -                        p                                                                              )                                2                            +                                                (                                                            y                      q                                        -                                          y                                              ci                        -                        p                                                                              )                                2                                      }                                              (        13        )            
There is estimated a mean value and a standard deviation of the brightness of the background in the estimated picture element location 22 based on the equation (14), using the distance dq,ci-p.                                           μ            q                    =                                    {                                                ∑                                      i                    -                    pεψ                                                  ⁢                                                                            μ                                              i                        -                        p                                                              ⁡                                          (                                              d                                                  q                          ,                                                      ci                            -                            p                                                                                              )                                                                            -                    2                                                              }                        /                          {                                                ∑                                      i                    -                    pεψ                                                  ⁢                                                      (                                          d                                              q                        ,                                                  ci                          -                          p                                                                                      )                                                        -                    2                                                              }                                      ⁢                  
                ⁢                              σ            q                    =                                    {                                                ∑                                      i                    -                    pεψ                                                  ⁢                                                                            σ                                              i                        -                        p                                                              ⁡                                          (                                              d                                                  q                          ,                                                      ci                            -                            p                                                                                              )                                                                            -                    2                                                              }                        /                          {                                                ∑                                      i                    -                    pεψ                                                  ⁢                                                      (                                          d                                              q                        ,                                                  ci                          -                          p                                                                                      )                                                        -                    2                                                              }                                                          (        14        )            
Here, the denominator is the term for normalization. Consequently, in the equation (14), the estimation is implemented in such a way that it is weighted to be averaged by quantity in proportion to the squared reciprocal of distance. There is obtained the mean value and the standard deviation of the brightness by way of the background in terms of the whole pixels by repeating above procedure. It is capable of being used a reciprocal or a cube of reciprocal for method of adding weight.
According to a twenty ninth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the statistic estimation process judges a location of the center of gravity of the sectional image including only the background as the location of the center of gravity of an object, when estimating a mean value and a standard deviation of the prescribed characteristic value by way of the background in respective pixels on an image, thus supposing a straight line connecting the pixel and the location of the center of gravity of the object, and a half straight line located at opposite side of the location of the center of gravity of the object from the pixel on the straight line, subsequently, selecting whole center pixels of the sectional image including only the background, which sectional image intersected location is located on the half straight line while dropping a perpendicular to the straight line from the center pixel of the sectional image including only the background, then, calculating a distance between the pixel and the location of the center pixel of the sectional image including only the background selected previously, thus implementing estimation of a mean value and a standard deviation of the prescribed characteristic value of the background at the location of the pixel on the image, due to the fact that the mean value and standard deviation of the prescribed characteristic value of the background of the location of the center pixel in the sectional image including only the background selected previously are weighted to average in answer to the corresponding distance.
In the twenty fourth aspect of the invention, the object detectable and background removal method estimates the statistics in the whole picture from the sectional image including only the background, in the process of the twenty fourth aspect concerned, in addition thereto, in the object detectable and background removal method of the twenty ninth aspect, the statistic estimation process judges a location of the center of gravity of the sectional image including only the background as the location of the center of gravity of an object, when estimating a mean value and a standard deviation of the brightness by way of the background in respective pixels on an image, thus supposing a straight line connecting the pixel and the location of the center of gravity of the object, and a half straight line located at opposite side of the location of the center of gravity of the object from the pixel on the straight line, subsequently, selecting whole center pixels of the sectional image including only the background, which sectional image intersected location is located on the half straight line while dropping a perpendicular to the straight line from the center pixel of the sectional image including only the background, then, calculating a distance between the pixel and the location of the center pixel of the sectional image including only the background selected previously, thus implementing estimation of a mean value and a standard deviation of the brightness of the background at the location of the pixel on the image, due to the fact that the mean value and standard deviation of the brightness of the background of the location of the center pixel in the sectional image including only the background selected previously are weighted to average in answer to the corresponding distance.
Namely, the invention of the twenty ninth aspect utilizes only the mean value and the standard deviation of the brightness of the sectional image including only the background, which the mean value and the standard deviation are located at the opposite side of the object center of gravity location when there is obtained a mean value and a standard deviation of the brightness by way of the background of a pixel location, in comparison with the twenty eighth aspect. The invention of the twenty ninth aspect will be described referring to FIG. 10.
Firstly, there is obtained the object center of gravity location 20 while calculating the center of gravity location of the sectional image including only the background. When there is obtained the mean value and the standard deviation of the brightness by way of the background in the estimated pixel location 22, supposing a straight line 23 connecting the estimated pixel location 22 and the object center of gravity location 20.
There is dropped the straight lire 24 to be the perpendicular line from the center pixel location cl-B of the sectional image to the straight line 23 while taking notice of 1-B of the sectional images including only the background. Since the intersection point of the straight line 23 and the straight line 24 belongs to a half straight line located at opposite side of the object center of gravity location 20 from the estimated pixel location 22 of the straight line 23, putting it into the set "psgr" of the sectional image for utilizing in case of estimation of the sectional image 1-B. Above operation is repeated in terms of the whole sectional images including only the background, before implementing the estimation processing based on the equation (14).
The invention of the twenty ninth aspect repeats the present estimation processing over the whole pixels.
According to a thirtieth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein the statistic estimation process is provided with a location of the center of gravity of an object which comes to be the center of gravity of an object beforehand.
In the twenty sixth aspect to the twenty ninth aspect of the invention, the object detectable and background removal method estimates the statistics in the whole picture from the sectional image including only the background, in the process of the twenty sixth aspect to the twenty ninth aspect concerned, in addition thereto, in the object detectable and background removal method of the thirtieth aspect, the object center of gravity location to be the center of gravity of the object is given beforehand.
According to a thirty first aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein in regard to isolation of only the object of detection target at the comparison process, when the comparison process implements threshold processing in the respective location of the pixels, firstly a first threshold is calculated to be defined in such a way that a constant set beforehand is multiplied by the standard deviation of the prescribed characteristic value of the background estimated previously, then the above multiplied number is subtracted from a mean value of the prescribed characteristic value of the background estimated previously, secondly a second threshold is calculated to be defined in such a way that also a constant set beforehand is multiplied by the standard deviation of the prescribed characteristic value of the background estimated previously, then the above multiplied number is added to a mean value of the prescribed characteristic value of the background estimated previously, thus in case where the prescribed characteristic value of the location of the pixel is lager than the first threshold and is smaller than the second threshold, judging the pixel as the background, so that the comparison process removes the background to isolate the object due to the fact that the comparison process causes the same processing to be executed over the whole pixels.
In the second aspect to the thirtieth aspect of the invention, the object detectable and background removal method determines the threshold over the whole picture from the statistics estimated previously, subsequently, comparing the determined threshold over the whole picture with the input image, thus isolating only the detection target object, in the process of the second aspect to the thirtieth aspect concerned, in addition thereto, in the object detectable and background removal method of the thirty first aspect, in regard to isolation of only the object of detection target at the comparison process, when the comparison process implements threshold processing in the respective location of the pixels, firstly a first threshold is calculated to be defined in such a way that a constant set beforehand is multiplied by the standard deviation of the brightness of the background estimated previously, then the above multiplied number is subtracted from a mean value of the brightness of the background estimated previously, secondly a second threshold is calculated to be defined in such a way that also a constant set beforehand is multiplied by the standard deviation of the brightness of the background estimated previously, then the above multiplied number is added to a mean value of the brightness of the background estimated previously, thus in case where the brightness of the location of the pixel is lager than the first threshold and is smaller than the second threshold, judging the pixel as the background, so that the comparison process removes the background to isolate the object due to the fact that the comparison process causes the same processing to be executed over the whole pixels.
Namely, the invention of the thirty first aspect removes only the background area to isolate the object to be the detection target as far as contours accurately by calculating the threshold based on the mean value and the standard deviation of the brightness of the background estimated previously from the input image.
The brightness values of the background scatter in the vicinity of the mean value of the brightness. The size of dispersion is capable of being estimated by the standard deviation. According to table 1 of normal distribution function described in the literature: xe2x80x9cMathematical Statisticsxe2x80x9d written by T. Takeuchi, published by Toyo Keizai, 1963, pp.361, there can be read the probability that the slipping off from the mean value disperses more than three times of the standard deviation is 0.26%. There is expressed in different words, when the brightness value of the background is in a state of the normal distribution, 99.74% of the pixel in the whole picture elements belong to the inside of three times of a mean valuexc2x1a standard deviation.
Consequently, the first threshold for judging whether or not a pixel located certain coordinates position (x, y) is a background is calculated by a equation (15) using a constant xcex1xe2x88x92 whose value is positive or zero determined beforehand, a mean value xcexci-p in the sectional image i-p including the pixel location, and the standard deviation "sgr"i-p.                               τ                      1            ,            x            ,            y                          =                              μ                          i              -              p                                -          α          -                      σ                          i              -              p                                                          (        15        )            
Similarly, the second threshold is calculated by a equation (16) using a constant xcex1+ whose value is positive or zero determined beforehand.                               τ                      2            ,            x            ,            y                          =                              μ                          i              -              p                                -          α          +                      σ                          i              -              p                                                          (        16        )            
It becomes possible to distinguish only the pixels constituting the background due to the threshold processing of a equation (17) utilizing above two thresholds.                               τ                      1            ,            x            ,            y                          ≦                  I                      x            ,            y                          ≦                  τ                      2            ,            x            ,            y                                              (        17        )            
The invention of the thirty first aspect is described by using the arithmetic mean or the standard deviation, however it is capable of constituting the invention of the aspect by using a geometric mean, a harmonic mean, a median and so forth which are statistical estimation amount to be representative value, and the statistics representing dispersion such as an absolute deviation, and a quarter deviation described in the literature: xe2x80x9cEncyclopedia of Mathematical Sciencesxe2x80x9d published by Maruzen Co., Ltd, 1991, pp. 495.
According to a thirty second aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein in regard to isolation of only the object of detection target at the comparison process, the comparison process implements a threshold processing over the whole pixels, to detect areas linked one another by way of areas to be a candidate of a background, thus judging the areas concerned as areas including the most large number of sectional images whose probability of including only the background among candidate areas of the background.
In the thirty first aspect of the invention, the object detectable and background removal method determines the threshold in the whole picture from the statistics described above, subsequently, comparing the threshold in the whole picture determined previously with the input image, thus isolating only the object to be the detection target, in the process of the twenty first aspect concerned, the object detectable and background removal method of the thirty second aspect implements the threshold processing over the whole pixel, before detecting the areas linked with each other by way of the area to be the candidate of the background, thus removing the background to isolate the object due to the fact that there is taken the candidate area including the largest number of the sectional images whose probability of including only the background to be the background area among the candidate areas of the background.
Namely, the invention of the thirty second aspect removes the background to isolate the object due to the fact that there is taken the candidate area including the largest number of the sectional images whose probability of including only the background. In virtue of this matter, even though when the brightness distribution of the background is close to the brightness distribution of the inside of object, there is prevented that a part of the inside of the object is wrongly removed as the background.
According to a thirty third aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein in regard to isolation of only the object of detection target at the comparison process, the comparison process implements a threshold processing over the whole pixels, to detect areas linked one another by way of areas to be a candidate of a background, thus judging the areas concerned as areas including the most large number of sectional images whose probability of including only the background among candidate areas of the background.
In the thirty first aspect of the invention, the object detectable and background removal method determines the threshold in the whole picture from the statistics described above, subsequently, comparing the threshold in the whole picture determined previously with the input image, thus isolating only the object to be the detection target, in the process of the twenty first aspect concerned, the object detectable and background removal method of the thirty second aspect implements the threshold processing over the whole pixel, before detecting the areas linked with each other by way of the area to be the candidate of the background, thus removing the background to isolate the object due to the fact that there is taken the candidate area including the largest number of the sectional images including only the background to be the background area among the candidate areas of the background. According to a thirty fourth aspect of the invention, there is provided a method of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein in regard to isolation of only the object of detection target at the comparison process, the comparison process implements a threshold processing over the whole pixels, to detect areas linked one another by way of areas to be a candidate of a background, thus judging the area concerned including the most smallest number of sectional images which include image with the exception of the background among the candidate areas of the background as a background area, so that the comparison process removes the background to isolate the object.
In the thirty first aspect of the invention, the object detectable and background removal method determines the threshold in the whole picture from the statistics described above, subsequently, comparing the threshold in the whole picture determined previously with the input image, thus isolating only the object to be the detection target, in the process of the twenty first aspect concerned, the object detectable and background removal method of the thirty second aspect implements the threshold processing over the whole pixel, before detecting the areas linked with each other by way of the area to be the candidate of the background, thus removing the background to isolate the object due to the fact that there is taken the candidate area including the smallest number of sectional images except the background among the candidate areas of the background to be a background area.
According to a thirty fifth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours comprising a sectional image statistic calculation means for calculating a mean value and a standard deviation of the prescribed characteristic value of the sectional image while dividing to be processed an input image into sectional images, a background sectional image selection means judging a sectional image whose standard deviation is of the smallest value in the sectional images as a sectional image whose probability of including only a background, subsequently, comparing a standard deviation of the prescribed characteristic value of the sectional image with a standard deviation of the prescribed characteristic value of another sectional images, thus judging a sectional image having a standard deviation whose difference between the standard deviation concerned and another standard deviation is less than a threshold to be a sectional image including only the background, a background statistic estimation means for investigating all of mean values and standard deviations in the sectional images including only the background and in another sectional images by way of the background estimated previously, further in the sectional images including images with the exception of said background and in the sectional images including only the background located in the neighborhood of the sectional image, and in the sectional image by way of the background estimated previously in another sectional image, and a threshold generation object detectable and background removal means wherein in order to isolate an object to be removed background by using the mean value and the standard deviation in the whole sectional images, a second threshold is calculated to be defined in such a way that also a constant set beforehand is multiplied by the standard deviation of the prescribed characteristic value of the background estimated previously, then the above multiplied number is added to a mean value of the prescribed characteristic value of the background estimated previously, subsequently, calculating it all over the pictures to be outputted, thus judging pixels within the threshold as a background while using said two kinds of thresholds and judging pixels without the threshold as an object of detection target.
According to a thirty sixth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, with an image constituted by virtually even background and an object of detection target, the device roughly consisting of a sectional image statistic calculation means, a background sectional image selection means, a background statistic estimation means, and a threshold generation object detectable and background removal means, the sectional image statistic calculation means comprising a sectional image division means for dividing input images into sectional images, a mean value and a standard deviation calculation means which calculates to be outputted a mean value and a standard deviation of the prescribed characteristic value in every respective sectional images with the sectional image signals as inputs, and a sectional image statistic storage means storing to be outputted the mean value and the standard deviation of the prescribed characteristic value of respective sectional images with the mean value and the standard deviation of the prescribed characteristic value of the sectional images as inputs, said background sectional image selection means comprising a minimum standard deviation reference background only sectional image selection means for outputting a sectional image whose standard deviation of the prescribed characteristic value is of the most smallest value among sectional images as a sectional image whose probability of including only a background with the mean value and the standard deviation of the prescribed characteristic value of the sectional image, a background only sectional image selection means comparing a standard deviation of the prescribed characteristic value of a sectional image whose probability of including only the background with a standard deviation of the prescribed characteristic value in another sectional images, thus judging to be outputted a partial image having standard deviation whose difference between the standard deviation concerned and a standard deviation of the prescribed characteristic value of a sectional image whose probability of including only the background is less than a threshold as a sectional image including only a background and a background only sectional image statistic storage means storing a location of the sectional image including only the background and the mean value and the standard deviation of the prescribed characteristic value of the sectional image concerned to output them at any time, the background statistic estimation means comprising a background exception sectional image selection means, when a command for investigating a sectional image including image with the exception of a background comes thereto, investigating both of a mean value and a standard deviation of the prescribed characteristic value in a sectional image including only a background and a mean value and a standard deviation of the prescribed characteristic value by way of an estimated background in another sectional images, if there exists a sectional image whose no estimated value of a mean value and a standard deviation of the prescribed characteristic value by way of a background exists, outputting the partial image concerned, in case where a mean value and a standard deviation of the prescribed characteristic value are estimated with regard to whole sectional images, so that the background statistic estimation means issues a command of generating a threshold for the sake of object detectable and background removal, a neighborhood background only sectional image existence judgement means investigating mean values and standard deviations both of sectional images including images with the exception of a background and sectional images including only a background located in the neighborhood of the sectional images, and investigating mean values and standard deviations of the prescribed characteristic value by way of a background estimated previously in another sectional images, when there exists a sectional image whose only one set of a mean value and a standard deviation of the prescribed characteristic value are estimated in the neighborhood thereof, thus issuing a command so as to estimate a mean value and a standard deviation of the prescribed characteristic value of a sectional image including image with the exception of the background, a mean value and a standard deviation interpolation/extrapolation means, when receiving a command to estimate a mean value and a standard deviation of the prescribed characteristic value in the sectional image including image with the exception of the background, estimating to be outputted by averaging both of mean values and standard deviations of the prescribed characteristic value of a sectional image including only the background in the neighborhood thereof, simultaneously, outputting an estimated sectional image selection command signal so as to select next sectional image, and an estimated statistic storage means storing the mean value and the standard deviation of the prescribed characteristic value estimated previously to be outputted whenever necessary, and the threshold generation object detectable and background removal means comprising a threshold generation means, when a command for calculating a threshold is entered therein after completing whole mean values and standard deviations of the prescribed characteristic value in the whole sectional images, by using the mean value and the standard deviation in the whole sectional images, a second threshold is calculated to be defined in such a way that also a constant set beforehand is multiplied by the standard deviation of the prescribed characteristic value of the background estimated previously, then the above multiplied number is added to a mean value of the prescribed characteristic value of the background estimated previously, subsequently, calculating it all over the pictures to be outputted, and a threshold processing means judging pixels within the threshold as a background while using the two kinds of thresholds and judging pixels without the threshold as an object of detection target.
According to a thirty seventh aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the prescribed characteristic value is at least one of a brightness, a color information, and an edge information.
According to a thirty eighth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means comprises a minimum standard deviation reference background only sectional image selection means outputting partial images in such a way that it causes the sectional images of the specified number in order of the smaller number of a standard deviation of the prescribed characteristic value to be outputted while taking such sectional images to be the sectional image whose probability of including only the background with the mean value and the standard deviation of the prescribed characteristic value of the sectional image as inputs, a background only sectional image selection means compares a standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background with a standard deviation of the prescribed characteristic value of another sectional images, thus judging to be outputted the sectional image having a standard deviation whose difference between the standard deviation concerned and a standard deviation of the prescribed value of the sectional image whose probability of including only the background is high as a sectional image including only a background, and a background only sectional image statistic storage means storing the location of the sectional image including only the background and the mean value and the standard deviation of the prescribed characteristic value of the sectional image concerned, thus outputting them whenever necessary.
According to a thirty ninth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means further comprises a standard deviation difference background only sectional image selection means selecting to be outputted a sectional image whose standard deviation is of the most nearest value of the standard deviation of the prescribed characteristic value instructed beforehand, a background only sectional image selection means comparing a standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background with a standard deviation of the prescribed characteristic value of another sectional images, thus judging to be outputted the sectional image having a standard deviation whose difference between the standard deviation concerned and a standard deviation of the prescribed value of the sectional image whose probability of including only the background is high as a sectional image including only a background, and a background only sectional image statistic storage means storing the location of the sectional image including only the background and the mean value and the standard deviation of the prescribed characteristic value of the sectional image concerned, thus outputting them whenever necessary.
According to a fortieth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means comprises a standard deviation reference background only sectional image selection means which outputs sectional images as many as the number specified in order of the most nearest value to the standard deviation of the skewness of the prescribed characteristic value instructed beforehand, judging the sectional image concerned as the sectional image whose probability of including only the background is high, a background only sectional image selection means comparing a standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background with a standard deviation of the prescribed characteristic value of another sectional images, thus judging to be outputted the sectional image having a standard deviation whose difference between the standard deviation concerned and a standard deviation of the prescribed value of the sectional image whose probability of including only the background is high as a sectional image including only a background, and a background only sectional image statistic storage means storing the location of the sectional image including only the background and the mean value and the standard deviation of the prescribed characteristic value of the sectional image concerned, thus outputting them whenever necessary.
According to a forty first aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as concurs, wherein the background sectional image selection means comprises a mean value and standard deviation reference background only sectional image selection means outputting a sectional image whose standard deviation is of the most nearest value to the mean value and the standard deviation of the prescribed characteristic value instructed beforehand as a sectional image whose probability of including only a background, a background only sectional image selection means comparing a standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background with a standard deviation of the prescribed characteristic value of another sectional images, thus judging to be outputted the sectional image having a standard deviation whose difference between the standard deviation concerned and a standard deviation of the prescribed value of the sectional image whose probability of including only the background is high as a sectional image including only a background, and a background only sectional image statistic storage means storing the location of the sectional image including only the background and the mean value and the standard deviation of the prescribed characteristic value of the sectional image concerned, thus outputting them whenever necessary.
According to forty second aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as an outline, wherein said background sectional image selection means comprises a mean value and standard deviation reference background only sectional image selection means which outputs sectional images as many as the number specified in order of nearer number to the mean value and the standard deviation of the prescribed characteristic value instructed beforehand as a sectional image whose probability of including only a background, a background only sectional image selection means comparing a standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background with a standard deviation of the prescribed characteristic value of another sectional images, thus judging to be outputted the sectional image having a standard deviation whose difference between the standard deviation concerned and a standard deviation of the prescribed value of the sectional image whose probability of including only the background is high as a sectional image including only a background, and a background only sectional image statistic storage means storing the location of the sectional image including only the background and the mean value and the standard deviation of the prescribed characteristic value of the sectional image concerned, thus outputting them whenever necessary.
According to a forty third aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means selects a sectional image whose probability of including only a background is high from sectional image involved in an area instructed beforehand.
According to a forty fourth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means selects a sectional image whose probability of including only a background is high from sectional images involved in a plurality of areas instructed beforehand.
According to a forty fifth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, with an image constituted by virtually even background and an object of detection target, the device roughly consisting of four means of a sectional image statistic calculation means, a background sectional image selection means, a background statistic estimation means, and a threshold generation object detectable and background removal means, the sectional image statistic calculation means comprises a sectional image division means performing division output of input image into sectional images, a mean value and standard deviation and skewness calculation means calculating to be outputted a mean value, a standard deviation, and a skewness of a prescribed characteristic value in every respective sectional images with the sectional image signal as inputs, a sectional image statistic storage means storing to be outputted whenever necessary a mean value, a standard deviation, and a skewness of the prescribed characteristic value of respective sectional images with the mean value, the standard deviation, and the skewness of the prescribed characteristic value of the sectional images as inputs, and the background sectional image selection means comprises a skewness threshold and minimum standard deviation reference background only sectional image selection means outputting a sectional image whose absolute value of the skewness is less than a threshold given beforehand in sectional images, and whose standard deviation of the prescribed characteristic value is of the smallest value as a sectional image whose probability of including only a background, a background only sectional image selection means judging to be outputted a sectional image having a standard deviation whose difference between the standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background is high and the standard deviation of the prescribed characteristic value in the sectional images is less than the threshold, and outputting a sectional image whose absolute value of the skewness is less than a threshold given beforehand as a sectional image including only a background, and a background only sectional image statistic storage means storing the location of the sectional image including only the background and the mean value and the standard deviation of the prescribed characteristic value of the sectional image concerned, thus outputting them whenever necessary; and the background statistic estimation means comprises a background exception sectional image selection means, when a command for investigating sectional images including images with the exception of a background, investigating a mean value and a standard deviation of the prescribed characteristic value of the sectional image including only the background and a mean value and a standard deviation of the prescribed characteristic value by way of estimated background of another sectional images, if there exists a sectional image whose mean value and standard deviation of the prescribed characteristic value by way of the background is not estimated, outputting the sectional image concerned, while if the mean value and the standard deviation of the prescribed characteristic value by way of the background in respect to whole sectional images are instructed, issuing a command so as to generate a threshold for the sake of object detectable and background removal, a neighborhood background only sectional image existence judgement means investigating all of mean values and standard deviations of the prescribed characteristic values both of sectional images including images with the exception of backgrounds and sectional images including only background located in the neighborhood of the sectional images, and mean values and standard deviations of the prescribed characteristic value by way of the estimated background of another sectional images, even though when there exists only one sectional image whose mean value and standard deviation of the prescribed characteristic value in the neighborhood, issuing a command so as to estimate a mean value and a standard deviation of the prescribed characteristic value of the sectional image including images with the exception of the background, while when there exist no sectional image whose mean value and standard deviation of the prescribed characteristic value are estimated, issuing a command so as to select next sectional image, a mean value and standard deviation interpolation extrapolation means, when receiving a command for estimating a mean value and a standard deviation of the prescribed characteristic value in the sectional image including images with the exception of the background, thus estimating to be outputted by averaging the mean value and the standard deviation of the prescribed characteristic value of the sectional image including only the background in the neighborhood thereof, and by averaging the mean value and the standard deviation of the prescribed characteristic value by way of the estimated background of the sectional image in the neighborhood thereof, simultaneously outputting an estimated sectional image selection command signal so as to select next sectional image, and an estimated statistic storage means storing to be outputted the mean value and the standard deviation of the prescribed characteristic value estimated previously; the threshold generation object detectable and background removal means comprises a threshold generation means, when receiving a command for calculating threshold after the mean value and the standard deviation of the prescribed characteristic value by way of the background in the whole sectional image had been estimated, a first threshold is obtained in such a way that it causes a standard deviation multiplied by a constant given beforehand to be subtracted from the mean value by using the mean value and the standard deviation of the prescribed characteristic by way of the estimated background in the whole sectional images for the sake of detecting object and removing background, and a second threshold is obtained in such a way that it causes a standard deviation multiplied by a constant given beforehand to be added to the mean value, thus the first and the second threshold are calculated over whole picture to be outputted, and a threshold processing means taking pixels involved between two thresholds to be a background, and taking another pixels to be an object of detection target by using the two thresholds.
According to a fourth sixth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means comprises a skewness threshold background only sectional image selection means judging to be outputted the sectional image whose absolute value of skewness is less than the threshold given beforehand among the sectional images as a sectional image whose probability of including only a background with the mean value and the standard deviation of the prescribed characteristic value of the sectional image and the skewness of the sectional image as inputs, a background only sectional image selection means outputting a sectional image having a standard deviation whose difference between the standard deviation thereof and the standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background is less than threshold given beforehand and whose absolute value of skewness is less than the threshold as a sectional image including only the background while comparing a standard deviation of the prescribed characteristic value of the sectional image whose probability of including the background with the standard deviation of the prescribed characteristic value in another sectional images, and a background only sectional image statistic storage means storing the mean value and the standard deviation of the prescribed characteristic value of a location of the sectional image including only the background and the sectional image concerned to be outputted whenever necessary.
According to a forty seventh aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means comprises a skewness threshold and minimum standard deviation reference background only sectional image selection means outputting sectional images whose absolute value of skewness is less than the threshold given beforehand and whose number is specified in order of smaller value of a standard deviation of the prescribed characteristic value as a sectional image whose probability of including only a background with a mean value and a standard deviation of the prescribed characteristic value of sectional images and a skewness of sectional images as inputs, a background only sectional image selection means outputting a sectional image having a standard deviation whose difference between the standard deviation thereof and the standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background is less than threshold given beforehand and whose absolute value of skewness is less than the threshold as a sectional image including only the background while comparing a standard deviation of the prescribed characteristic value of the sectional image whose probability of including the background with the standard deviation of the prescribed characteristic value in another sectional images, and a background only sectional image statistic storage means storing the mean value and the standard deviation of the prescribed characteristic value of a location of the sectional image including only the background and the sectional image concerned to be outputted whenever necessary.
According to a forty eighth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means comprises a skewness threshold and standard deviation reference background only sectional image selection means outputting a sectional image whose absolute value of skewness is less than the threshold value and whose standard deviation is the most nearest value of the standard deviation of the prescribed characteristic value instructed beforehand among sectional images as a sectional image whose probability of including only the background with a mean value and a standard deviation of the prescribed characteristic value of sectional images and a skewness of sectional images as inputs, a background only sectional image selection means outputting a sectional image having a standard deviation whose difference between the standard deviation thereof and the standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background is less than threshold given beforehand and whose absolute value of skewness is less than the threshold as a sectional image including only the background while comparing a standard deviation of the prescribed characteristic value of the sectional image whose probability of including the background with the standard deviation of the prescribed characteristic value in another sectional images, and a background only sectional image statistic storage means storing the mean value and the standard deviation of the prescribed characteristic value of a location of the sectional image including only the background and the sectional image concerned to be outputted whenever necessary.
According to a forty ninth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means comprises a skewness threshold and standard deviation reference background only sectional image selection means outputting a sectional image whose absolute value of skewness is less than the threshold value and whose number is specified in order of nearer value to the standard deviation of the prescribed characteristic value instructed beforehand among sectional images as a sectional image whose probability of including only the background with a mean value and a standard deviation of the prescribed characteristic value of sectional images and a skewness of sectional images as inputs, a background only sectional image selection means outputting a sectional image having a standard deviation whose difference between the standard deviation thereof and the standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background is less than threshold given beforehand and whose absolute value of skewness is less than the threshold as a sectional image including only the background while comparing a standard deviation of the prescribed characteristic value of the sectional image whose probability of including the background with the standard deviation of the prescribed characteristic value in another sectional images, and a background only sectional image statistic storage means storing the mean value and the standard deviation of the prescribed characteristic value of a location of the sectional image including only the background and the sectional image concerned to be outputted whenever necessary.
According to a fiftieth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means comprises a skewness threshold and mean value and standard deviation reference background only sectional image selection means outputting a sectional image whose absolute value of skewness is less than the threshold value and whose mean value and standard deviation are of the most nearest values of the mean value and standard deviation of the prescribed characteristic value instructed beforehand among sectional images as a sectional image whose probability of including only the background with a mean value and a standard deviation of the prescribed characteristic value of sectional images and a skewness of sectional images as inputs, a background only sectional image selection means outputting a sectional image having a standard deviation whose difference between the standard deviation thereof and the standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background is less than threshold given beforehand and whose absolute value of skewness is less than the threshold as a sectional image including only the background while comparing a standard deviation of the prescribed characteristic value of the sectional image whose probability of including the background with the standard deviation of the prescribed characteristic value in another sectional images, and a background only sectional image statistic storage means storing the mean value and the standard deviation of the prescribed characteristic value of a location of the sectional image including only the background and the sectional image concerned to be outputted whenever necessary.
According to a fifty first aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means comprises a skewness threshold and mean value and standard deviation reference background only sectional image selection means outputting a sectional image whose absolute value of skewness is less than the threshold value and whose number is specified in order of nearer value of the mean value and the standard deviation of the prescribed characteristic value instructed beforehand among sectional images as a sectional image whose probability of including only the background with a mean value and a standard deviation of the prescribed characteristic value of sectional images and a skewness of sectional images as inputs, a background only sectional image selection means outputting a sectional image having a standard deviation whose difference between the standard deviation thereof and the standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background is less than threshold given beforehand and whose absolute value of skewness is less than the threshold as a sectional image including only the background while comparing a standard deviation of the prescribed characteristic value of the sectional image whose probability of including the background with the standard deviation of the prescribed characteristic value in another sectional images, and a background only sectional image statistic storage means storing the mean value and the standard deviation of the prescribed characteristic value of a location of the sectional image including only the background and the sectional image concerned to be outputted whenever necessary.
According to a fifty second aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means comprises a skewness threshold background only sectional image selection means outputting a sectional image whose absolute value of skewness is less than the threshold given beforehand and whose probability of including only background is of the most highest value among sectional images as a sectional image whose probability of including only a background with a mean value and a standard deviation of the prescribed characteristic value of sectional images and skewness of sectional images, and probability of including only a background given in every sectional images within the area instructed beforehand as inputs, a background only sectional image selection means outputting a sectional image having a standard deviation whose difference between the standard deviation thereof and the standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background is less than threshold given beforehand and whose absolute value of skewness is less than the threshold as a sectional image including only the background while comparing a standard deviation of the prescribed characteristic value of the sectional image whose probability of including the background with the standard deviation of the prescribed characteristic value in another sectional images, and a background only sectional image statistic storage means storing the mean value and the standard deviation of the prescribed characteristic value of a location of the sectional image including only the background and the sectional image concerned to be outputted whenever necessary.
According to a fifty third aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means comprises a skewness threshold background only sectional image selection means outputting a sectional image whose absolute value of skewness is less than the threshold given beforehand and whose probability of including only background is of the most highest value among sectional images as a sectional image whose probability of including only a background with a mean value and a standard deviation of the prescribed characteristic value of sectional images and skewness of sectional images, and probability of including only a background given in every sectional images within the area instructed beforehand as inputs, a background only sectional image selection means outputting a sectional image having a standard deviation whose difference between the standard deviation thereof and the standard deviation of the prescribed characteristic value of the sectional image whose probability of including only the background is less than threshold given beforehand and whose absolute value of skewness is less than the threshold as a sectional image including only the background while comparing a standard deviation of the prescribed characteristic value of the sectional image whose probability of including the background with the standard deviation of the prescribed characteristic value in another sectional images, and a background only sectional image statistic storage means storing the mean value and the standard deviation of the prescribed characteristic value of a location of the sectional image including only the background and the sectional image concerned to be outputted whenever necessary.
According to fifty fourth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means comprises a background exception sectional image selection means, when a command for investigating sectional images including images with the exception of a background, investigating a mean value and a standard deviation of the sectional image including only the background and a mean value and a standard deviation of the prescribed characteristic value by way of estimated background of another sectional images, if there exists a sectional image whose mean value and standard deviation of the prescribed characteristic value by way of the background is not estimated, outputting the sectional image concerned, while if the mean value and the standard deviation of the prescribed characteristic value by way of the background in terms of whole sectional images are estimated, issuing a command so as to generate a threshold for the sake of object detectable and background removal, a neighborhood background only sectional image existence judgement means investigating all of mean values and standard deviations of the prescribed characteristic values both of sectional images including images with the exception of backgrounds and sectional images including only background located in the neighborhood of the sectional images, and mean values and standard deviations of the prescribed characteristic value by way of the estimated background of another sectional images, even though when there exists only one sectional image whose mean value and standard deviation of the prescribed characteristic value in the neighborhood, issuing a command so as to estimate a mean value and a standard deviation of the prescribed characteristic value of the sectional image including images with the exception of the background, while when there exist no sectional image whose mean value and standard deviation of the prescribed characteristic value are estimated, issuing a command so as to select next sectional image, a mean value and standard deviation interpolation extrapolation means, when receiving a command for estimating a mean value and a standard deviation of the prescribed characteristic value in the sectional image including images with the exception of the background, thus estimating to be outputted by averaging the mean value and the standard deviation of the prescribed characteristic value of the sectional image including only the background in the neighborhood thereof, and by averaging the mean value and the standard deviation of the prescribed characteristic value by way of the estimated background of the sectional image in the neighborhood thereof, simultaneously outputting an estimated sectional image selection command signal so as to select next sectional image, and an estimated statistic storage means storing to be outputted the mean value and the standard deviation of the prescribed characteristic value estimated previously, thus selecting a sectional image whose probability of including only a background is high from sectional images involved within areas instructed beforehand.
According to a fifty fifth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background sectional image selection means selects a sectional image whose probability of including only a background is high from sectional images involved within a plurality of areas instructed beforehand.
According to fifty sixth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background statistic estimation means comprises a center of gravity location calculation means outputting a location of the center of gravity as a location of the center of gravity of an object of detection target while investigating the mean value and the standard deviation of the prescribed characteristic value in the sectional images including only the background, and while calculating a location of the center of gravity with a location of sectional images without a mean value and a standard deviation of the prescribed characteristic value investigated, a sectional image to center of gravity location distance calculation means calculating to be outputted a distance between a location of center pixel of respective sectional images and the location of the center of gravity concerned, with the location of the center of gravity of the object of detection target, a background exception sectional image selection means, when a command for investigating sectional images including images with the exception of a background, investigating a mean value and a standard deviation of the prescribed characteristic value of the sectional image including only the background and a mean value and a standard deviation of the prescribed characteristic value by way of estimated background of another sectional images, if there exists a sectional image whose mean value and standard deviation of the prescribed characteristic value by way of the background is not estimated, outputting the sectional image concerned, while if the mean value and the standard deviation of the prescribed characteristic value by way of the background in respect to whole sectional images are instructed, issuing a command so as to generate a threshold for the sake of object detectable and background removal, a neighborhood background only sectional image existence judgement means investigating all of mean values and standard deviations of the prescribed characteristic values both of sectional images including images with the exception of backgrounds and sectional images including only background located in the neighborhood of the sectional images, and mean values and standard deviations of the prescribed characteristic value by way of the estimated background of another sectional images, even though when there exists only one sectional image whose mean value and standard deviation of the prescribed characteristic value in the neighborhood, issuing a command so as to estimate a mean value and a standard deviation of the prescribed characteristic value of the sectional image including images with the exception of the background, while when there exist no sectional image whose mean value and standard deviation of the prescribed characteristic value are estimated, issuing a command so as to select next sectional image, a mean value and standard deviation interpolation/extrapolation means, when receiving a command for estimating a mean value and a standard deviation of the prescribed characteristic value in the sectional image including images with the exception of the background, thus estimating to be outputted by averaging the mean value and the standard deviation of the prescribed characteristic value of the sectional image including only the background in the neighborhood thereof, and by averaging the mean value and the standard deviation of the prescribed characteristic value by way of the estimated background of the sectional image in the neighborhood thereof, simultaneously outputting an estimated sectional image selection command signal so as to select next sectional image, and an estimated statistic storage means storing to be outputted the mean value and the standard deviation of the prescribed characteristic value estimated previously.
According to fifty seventh aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background statistic estimation means comprises a background exception sectional image selection means, when receiving a command for investigating sectional images including images with the exception of a background, investigating a mean value and a standard deviation of the sectional image including only the background and a mean value and a standard deviation of the prescribed characteristic value by way of the estimated background of another sectional images, if there exists a sectional image whose mean value and standard deviation of the prescribed characteristic value by way of the background is not estimated, outputting the sectional image concerned, while if the mean value and the standard deviation of the prescribed characteristic value by way of the background in terms of whole sectional images are estimated, issuing a command so as to generate a threshold for the sake of object detectable and background removal, a neighborhood background only sectional image existence judgement means investigating all of mean values and standard deviations of the prescribed characteristic values both of sectional images including images with the exception of backgrounds and sectional images including only background located in the neighborhood of the sectional images, and mean values and standard deviations of the prescribed characteristic value by way of the estimated background of another sectional images, even though when there exists only one sectional image whose mean value and standard deviation of the prescribed characteristic value by way of the background among the sectional images to be neighborhood relationship given beforehand in every respective sectional images, issuing a command so as to estimate a mean value and a standard deviation of the prescribed characteristic value of the sectional image including images with the exception of the background, while when there exist no sectional image whose mean value and standard deviation of the prescribed characteristic value are estimated, issuing a command so as to select next sectional image, a mean value and standard deviation interpolation extrapolation means, when receiving a command for estimating a mean value and a standard deviation of the prescribed characteristic value in the sectional image including images with the exception of the background, estimating to be outputted signal of a mean value and a standard deviation by averaging the mean value and the standard deviation of the prescribed characteristic value of the sectional image including only the background in the neighborhood thereof, and by averaging the mean value and the standard deviation of the prescribed characteristic value by way of the estimated background of the sectional image in the neighborhood thereof, simultaneously outputting an estimated sectional image selection command signal so as to select next sectional image, and an estimated statistic storage means storing to be outputted the mean value and the standard deviation of the prescribed characteristic value estimated previously.
According to a fifty eighth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background statistic estimation means comprises a background exception pixel selection means scanning a pixel successively in order of the command in every reception for investigating next pixel, when the pixel which is watched agrees with the center pixel of the sectional image including only the back ground, causing the mean value and the standard deviation of the prescribed characteristic value to be the statistic of the pixel in the sectional image including only the background, while when the pixel which is watched disagrees with the center pixel of the sectional image including only the back ground, outputting the location of the pixel, subsequently in case where the scanning is completed in terms of the whole pixels, issuing a command so as to generate a threshold for the sake of an object detectable and background removal, a background exception pixel distance calculation means calculating to be outputted a distance between the location of pixel which is watched and the location of the center pixel of the whole sectional images including only the background, a mean value and standard deviation interpolation extrapolation means implementing estimation of a mean value and a standard deviation of the prescribed characteristic value by way of the background in the watched location of the pixel, due to the fact that the mean value and standard deviation of the prescribed characteristic value of the sectional image including only the background are weighted to average in answer to the location of center pixel and watched location of pixel and the corresponding distance, thus issuing a command so as to select next pixel, and an estimated statistic storage means storing the mean value and the standard deviation of the prescribed characteristic value by way of the estimated background, and the mean value and the standard deviation of the prescribed characteristic value in the location of the center pixel of the sectional image including only the background, thus outputting whenever necessary.
According to a fifty ninth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background statistic estimation means comprises a center of gravity location calculation means which investigates the mean value and the standard deviation of the prescribed characteristic value in the sectional images including only a background, subsequently, which investigates a location of a sectional image having no mean value and no standard deviation of the prescribed characteristic value to calculate a location of the center of gravity, thus outputting it by way of a location of the center of gravity of an object of detection target, a background exception pixel selection means scanning a pixel successively in order of the command in every reception for investigating next pixel, when the pixel which is watched agrees with the center pixel of the sectional image including only the back ground, causing the mean value and the standard deviation of the prescribed characteristic value to be the statistic of the pixel in the sectional image including only the background, while when the pixel which is watched disagrees with the center pixel of the sectional image including only the back ground, outputting the location of the pixel, subsequently in case where the scanning is completed in terms of the whole pixels, issuing a command so as to generate a threshold for the sake of an object detectable and background removal, a background only sectional image center pixel selection means supposing a straight line connecting the watched pixel and the location of the center of gravity of the object, and a half straight line located at opposite side of the location of the center of gravity of the object from the pixel on the straight line, subsequently, selecting whole center pixels of the sectional image including only the background, which sectional image intersected location is located on the half straight line while dropping a perpendicular to the straight line from the center pixel of the sectional image including only the background, a background exception pixel distance calculation means calculating to be outputted a distance between the watched location of the pixel and the location of center pixel of the selected sectional image including only the background, a mean value and standard deviation interpolation extrapolation means implementing estimation of a mean value and a standard deviation of the prescribed characteristic value by way of the background in the watched location of the pixel, due to the fact that the mean value and standard deviation of the prescribed characteristic value of the sectional image including only the background are weighted to average in answer to the location of center pixel and watched location of pixel and the corresponding distance, thus issuing a command so as to select next pixel, and an estimated statistic storage means storing the mean value and the standard deviation of the prescribed characteristic value by way of the estimated background, and the mean value and the standard deviation of the prescribed characteristic value in the location of the center pixel of the sectional image including only the background, thus outputting whenever necessary.
According to a sixtieth aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the background statistic estimation means does not possess a center of gravity location calculation means but a location of the center of gravity of an object to be the center of gravity of the object is given beforehand.
According to a sixty first aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the threshold generation object detectable and background removal means comprises a threshold generation means, when receiving a command for calculating threshold after the mean value and the standard deviation of the prescribed characteristic value by way of the background in the whole partial image had been estimated, a first threshold is obtained in such a way that it causes a standard deviation multiplied by a constant given beforehand to be subtracted from the mean value by using the mean value and the standard deviation of the prescribed characteristic by way of the estimated background in the whole sectional images for the sake of detecting object and removing background, and a second threshold is obtained in such a way that it causes a standard deviation multiplied by a constant given beforehand to be added to the mean value, thus the first and the second threshold are calculated over whole picture to be outputted, a threshold processing means taking pixels involved between two thresholds to be a background, a background candidate area detection means detecting area connecting together to the pixel judged as the background, by way of an area to be candidate of a background, and a background judgement means taking a candidate area including the greatest number of sectional images whose probability of including only the background is high to be background areas and taking another candidate areas to be target objects.
According to a sixty second aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contours, wherein the threshold generation object detectable and background removal means comprises a threshold generation means, when receiving a command for calculating threshold after the mean value and the standard deviation of the prescribed characteristic value by way of the background in the whole partial image had been estimated, a first threshold is obtained in such a way that it causes a standard deviation multiplied by a constant given beforehand to be subtracted from the mean value by using the mean value and the standard deviation of the prescribed characteristic by way of the estimated background in the whole sectional images for the sake of detecting object and removing background, and a second threshold is obtained in such a way that it causes a standard deviation multiplied by a constant given beforehand to be added to the mean value, thus the first and the second threshold are calculated over whole picture to be outputted, a threshold processing means taking pixels involved between two thresholds to be a background, a background candidate area detection means detecting area connecting together to the pixel judged as the background, by way of an area to be candidate of a background, and a background judgement means taking a candidate area including the greatest number of sectional images including only the background to be background areas and taking another candidate areas to be target objects.
According to a sixty third aspect of the invention, there is provided a device of object detectable and background removal for enabling an object to be automatically detected minutely and accurately as far as contour, wherein the threshold generation object detectable and background removal means comprises a threshold generation means, when receiving a command for calculating threshold after the mean value and the standard deviation of the prescribed characteristic value by way of the background in the whole partial image had been estimated, a first threshold is obtained in such a way that it causes a standard deviation multiplied by a constant given beforehand to be subtracted from the mean value by using the mean value and the standard deviation of the prescribed characteristic by way of the estimated background in the whole sectional images for the sake of detecting object and removing background, and a second threshold is obtained in such a way that it causes a standard deviation multiplied by a constant given beforehand to be added to the mean value, thus the first and the second threshold are calculated over whole picture to be outputted, a threshold processing means taking pixels involved between two thresholds to be a background, a background candidate area detection means detecting area connecting together to the pixel judged as the background, by way of an area to be candidate of a background, and a background judgement means taking a candidate area including the smallest number of sectional images including images with the exception of backgrounds to be background areas and taking another candidate areas to be target objects.
The above and further objects and novel features of the invention will be more fully understood from the following detailed description when the same is read in connection with the accompanying drawings. It should be expressly understood, however, that the drawings are for purpose of illustration only and are not intended as a definition of the limits of the invention.